## Kernel Density Estimation in Python

*implementations*of KDE currently available in Python. If you're unsure what kernel density estimation is, read Michael's post and then come back here.

There are several options available for computing kernel density estimates in Python. The question of the optimal KDE implementation for any situation, however, is not entirely straightforward, and depends a lot on what your particular goals are. Here are the four KDE implementations I'm aware of in the SciPy/Scikits stack:

In SciPy:

`gaussian_kde`

.In Statsmodels:

`KDEUnivariate`

and`KDEMultivariate`

(See an example here).In Scikit-learn:

`KernelDensity`

(See further examples here).

Each has advantages and disadvantages, and each has its area of applicability. I'll start with a table summarizing the strengths and weaknesses of each, before discussing each feature in more detail and running some simple benchmarks to gauge their computational cost: